Early attempts to train feed-forward models for style transfer focused on training a single model per style image or per artist/movement. Ulyanov et al. [1]} and Johnson et al. [2]} both proposed training models which take a single input (the content) and produce stylizations in one fixed target style. The training loss for these models was simply the original content and style loss proposed by [3]}. In a follow up work Ulyanov et al. [4]} proposed a mechanism for enabling style transfer algorithms to produce diverse outputs by using an extra noise vector as input, and adding an additional diversity term to the training objective to encourage outputs with different noise inputs (but the same content input) to be distant in pixel space. Wang et al. [5]} proposed an architecture that produces stylizations at multiple resolutions, and applies a separate style loss at each resolution to encourage capturing both large scale stylistic features and fine details such as brush strokes. Another work by Ulyanov et al. [6]} proposed Instance Normalization, normalizing an internal representation of the neural network for a particular example by subtracting by the mean and dividing the standard deviation of each channel computed over spatial locations. The purpose of this operation was to provide a closed form mechanism to put neural representations in a canonical 'style-free' feature space, which was easier to modify than the original neural representations which could have arbitrary mean and variance.
